Author archive for Ann Spencer, page 3

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?” with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points.

In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that may help data scientists, machine learning researchers, and engineers decide whether to trust the predictions of any classifier in any model, including seemingly “black box” models.

This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a rigorous evaluation of interpretability. Insights are drawn from Finale Doshi-Velez’s talk, “A Roadmap for the Rigorous Science of Interpretability” as well as the paper, “Towards a Rigorous Science of Interpretable Machine Learning”. The paper was co-authored by Finale Doshi-Velez and Been Kim. Finale Doshi-Velez is an assistant professor of computer science at Harvard Paulson School of Engineering and Been Kim is a research scientist at Google Brain.

This Domino Field Note provides highlights and excerpted slides from Amanda Casari’s “Feature Engineering for Machine Learning” talk at QCon Sao Paulo. Casari is the Principal Product Manager + Data Scientist at Concur Labs. Casari is also the co-author of the book, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. The full video of the talk is available here and special thanks to Amanda for providing permission to Domino to excerpt the talk’s slides in this Domino Field Note.

This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”. In his talk, Wickham advocates that, unlike a GUI, using code provides reproducibility, data provenance, and the ability to track changes so that data scientists have the ability to see how the data analysis has evolved. As the creator of ggplot2, it is not a surprise that Wickham also advocates the use of visualizations and models together to help data scientists find the real signals within their data. This blog post also provides clips from the original video and follows the Creative Commons license affiliated with the original video recording.